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The performance of coherent (and non-coherent) change detection algorithms is evaluated using complex SAR data that have been processed with various data compression approaches; the hope is that it may be possible to achieve higher compression ratios than could be achieved using classical image compression approaches such as BAQ (block adaptive quantization). BAQ compression is typically applied to raw (I,Q) SAR phase-history data, and our studies show that to obtain reasonably good coherent change detection (CCD) performance from a baseline CCD algorithm, BAQ compression requires at least 4-bit quantization for each of the I and Q phase-history data samples; since our original fullprecision data is 8-bits I and 8-bits Q, the best compression ratio (CR) that could be achieved using BAQ compression was a factor of 2. Our goal is to increase the amount of compression while achieving the same quality of change detection using more sophisticated wavelet-based approaches such as compressive sensing or set partitioning (SPIHT). This paper demonstrates a wavelet-based compressive sensing approach that gives CR = 3 with comparable CCD performance; we also demonstrate a wavelet-based SPIHT approach that gives CR = 4 with comparable CCD performance.

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The Lincoln Laboratory ground-based UWB Rail SAR was used to collect UHF and L-band data on a variety of mine-like targets. The target set consisted of metal pipes, bomb fragments, and M-20 metallic anti-tank mines, above and below ground. Mostly co-polarized data was collected for depression angles between 10 and 30 degrees. Imagery of the targets in different frequency sub-bands are shown and RCS characteristics are quantified.